from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-11-27 14:10:32.070475
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Fri, 27, Nov, 2020
Time: 14:10:36
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -42.7979
Nobs: 123.000 HQIC: -44.0198
Log likelihood: 1277.85 FPE: 3.31792e-20
AIC: -44.8556 Det(Omega_mle): 1.64190e-20
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.662351 0.198493 3.337 0.001
L1.Burgenland 0.137616 0.089538 1.537 0.124
L1.Kärnten -0.305830 0.074861 -4.085 0.000
L1.Niederösterreich 0.026044 0.215171 0.121 0.904
L1.Oberösterreich 0.271289 0.177229 1.531 0.126
L1.Salzburg 0.137388 0.089751 1.531 0.126
L1.Steiermark 0.077866 0.126962 0.613 0.540
L1.Tirol 0.173412 0.084083 2.062 0.039
L1.Vorarlberg 0.016902 0.082265 0.205 0.837
L1.Wien -0.165246 0.170178 -0.971 0.332
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.682588 0.257502 2.651 0.008
L1.Burgenland -0.000340 0.116156 -0.003 0.998
L1.Kärnten 0.338415 0.097115 3.485 0.000
L1.Niederösterreich 0.094925 0.279138 0.340 0.734
L1.Oberösterreich -0.247033 0.229916 -1.074 0.283
L1.Salzburg 0.179799 0.116433 1.544 0.123
L1.Steiermark 0.226851 0.164706 1.377 0.168
L1.Tirol 0.136634 0.109080 1.253 0.210
L1.Vorarlberg 0.204473 0.106722 1.916 0.055
L1.Wien -0.588037 0.220769 -2.664 0.008
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.333533 0.085403 3.905 0.000
L1.Burgenland 0.107900 0.038524 2.801 0.005
L1.Kärnten -0.024159 0.032209 -0.750 0.453
L1.Niederösterreich 0.136531 0.092579 1.475 0.140
L1.Oberösterreich 0.268018 0.076254 3.515 0.000
L1.Salzburg -0.008231 0.038616 -0.213 0.831
L1.Steiermark -0.061625 0.054626 -1.128 0.259
L1.Tirol 0.096652 0.036178 2.672 0.008
L1.Vorarlberg 0.150174 0.035395 4.243 0.000
L1.Wien 0.009885 0.073220 0.135 0.893
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.198777 0.101290 1.962 0.050
L1.Burgenland 0.007331 0.045691 0.160 0.873
L1.Kärnten 0.034915 0.038201 0.914 0.361
L1.Niederösterreich 0.086189 0.109800 0.785 0.432
L1.Oberösterreich 0.352938 0.090439 3.903 0.000
L1.Salzburg 0.088234 0.045799 1.927 0.054
L1.Steiermark 0.195902 0.064788 3.024 0.002
L1.Tirol 0.026504 0.042907 0.618 0.537
L1.Vorarlberg 0.115424 0.041980 2.750 0.006
L1.Wien -0.106948 0.086841 -1.232 0.218
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.820508 0.216540 3.789 0.000
L1.Burgenland 0.053655 0.097679 0.549 0.583
L1.Kärnten -0.012613 0.081667 -0.154 0.877
L1.Niederösterreich -0.100142 0.234735 -0.427 0.670
L1.Oberösterreich 0.060224 0.193342 0.311 0.755
L1.Salzburg 0.036276 0.097911 0.371 0.711
L1.Steiermark 0.102457 0.138505 0.740 0.459
L1.Tirol 0.224909 0.091728 2.452 0.014
L1.Vorarlberg 0.034492 0.089745 0.384 0.701
L1.Wien -0.189321 0.185650 -1.020 0.308
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.195826 0.150354 1.302 0.193
L1.Burgenland -0.041728 0.067823 -0.615 0.538
L1.Kärnten -0.013165 0.056705 -0.232 0.816
L1.Niederösterreich 0.196162 0.162988 1.204 0.229
L1.Oberösterreich 0.393715 0.134247 2.933 0.003
L1.Salzburg -0.037245 0.067985 -0.548 0.584
L1.Steiermark -0.056125 0.096171 -0.584 0.559
L1.Tirol 0.198061 0.063691 3.110 0.002
L1.Vorarlberg 0.055126 0.062314 0.885 0.376
L1.Wien 0.120421 0.128906 0.934 0.350
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.300826 0.191882 1.568 0.117
L1.Burgenland 0.066608 0.086556 0.770 0.442
L1.Kärnten -0.085734 0.072367 -1.185 0.236
L1.Niederösterreich -0.144977 0.208005 -0.697 0.486
L1.Oberösterreich -0.101052 0.171327 -0.590 0.555
L1.Salzburg -0.000079 0.086762 -0.001 0.999
L1.Steiermark 0.373040 0.122734 3.039 0.002
L1.Tirol 0.539768 0.081283 6.641 0.000
L1.Vorarlberg 0.232644 0.079526 2.925 0.003
L1.Wien -0.171419 0.164510 -1.042 0.297
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.124284 0.220147 0.565 0.572
L1.Burgenland 0.023624 0.099306 0.238 0.812
L1.Kärnten -0.062364 0.083027 -0.751 0.453
L1.Niederösterreich 0.255372 0.238645 1.070 0.285
L1.Oberösterreich 0.016638 0.196563 0.085 0.933
L1.Salzburg 0.230880 0.099543 2.319 0.020
L1.Steiermark 0.154619 0.140813 1.098 0.272
L1.Tirol 0.052651 0.093256 0.565 0.572
L1.Vorarlberg 0.013970 0.091240 0.153 0.878
L1.Wien 0.202024 0.188743 1.070 0.284
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.626403 0.123286 5.081 0.000
L1.Burgenland -0.006084 0.055613 -0.109 0.913
L1.Kärnten -0.001550 0.046497 -0.033 0.973
L1.Niederösterreich -0.062101 0.133645 -0.465 0.642
L1.Oberösterreich 0.276383 0.110079 2.511 0.012
L1.Salzburg 0.001976 0.055745 0.035 0.972
L1.Steiermark 0.007407 0.078857 0.094 0.925
L1.Tirol 0.075030 0.052225 1.437 0.151
L1.Vorarlberg 0.195602 0.051096 3.828 0.000
L1.Wien -0.103817 0.105699 -0.982 0.326
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.086368 -0.056725 0.193882 0.227759 0.013526 0.062064 -0.135831 0.103181
Kärnten 0.086368 1.000000 -0.071244 0.176067 0.069593 -0.168475 0.188070 0.010277 0.263332
Niederösterreich -0.056725 -0.071244 1.000000 0.226063 0.064579 0.153962 0.071617 0.048538 0.350956
Oberösterreich 0.193882 0.176067 0.226063 1.000000 0.243749 0.261817 0.072367 0.057776 0.040883
Salzburg 0.227759 0.069593 0.064579 0.243749 1.000000 0.140799 0.038148 0.077713 -0.067014
Steiermark 0.013526 -0.168475 0.153962 0.261817 0.140799 1.000000 0.094646 0.091500 -0.199898
Tirol 0.062064 0.188070 0.071617 0.072367 0.038148 0.094646 1.000000 0.133549 0.099408
Vorarlberg -0.135831 0.010277 0.048538 0.057776 0.077713 0.091500 0.133549 1.000000 0.080083
Wien 0.103181 0.263332 0.350956 0.040883 -0.067014 -0.199898 0.099408 0.080083 1.000000